Handling Missing Data and Drop Out in Hospice/Palliative Care Trials Through the Estimand Framework

Abstract

ContextMissing data are common in hospice/palliative care randomized trials due to high drop-out because of the demographic of interest. It can introduce bias in the estimate of the treatment effect and its precision. ObjectivesThe International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use (ICH) released updated guidance on statistical principles for clinical trials introducing the estimand framework to align trial objectives, trial conduct, statistical analysis and interpretation of results. Our objective is to present how the estimand framework can be used to guide the handling of missing data in palliative care trials. MethodsWe outline the estimand framework by highlighting the five elements of an estimand: treatment, population, variable, summary measure and intercurrent event handling. We list common intercurrent events in palliative care trials and present the five strategies for handling intercurrent events outlined in the ICH guidance. ResultsWe describe common intercurrent events in palliative care trials and discuss and justify what analytic strategies could be followed with each. We provide an example using a palliative care trial comparing two opioids for pain relieve in participants with cancer pain. ConclusionWhen planning a palliative care trial, the estimand should be explicitly stated, including how intercurrent events will be handled in the analysis. This should be informed by the scientific objectives of the trial. The estimand guides the handling of missing data during the conduct and analysis of the trial. Defining an estimand is not a statistical activity, but a multi-disciplinary process involving all stakeholders.